Frontiers in neurology · 2026
An interpretable multimodal ensemble assessment framework for Alzheimer's disease cognitive staging.
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Zhang Jiale, Yuan Bo, Liu Yaran, Xue Zhidong
Аннотация
OBJECTIVES: Alzheimer's disease (AD) is the most prevalent type of neuro-degenerative dementia. Artificial intelligence assisted clinical evaluation can improve diagnostic efficiency and facilitate timely intervention.
METHODS: An interpretable Multimodal Ensemble Assessment Framework (MEAF) was developed to support clinical evaluation for AD across the cognitive spectrum. This framework employed a Swin Transformer and ResNet-50 for staged modeling of imaging features, applied machine learning techniques to extract clinical features, and used decision-level ensemble learning to integrate multimodal predictions. For interpretability analysis, Gradient-weighted Class Activation Maps were used to highlight key brain regions contributing to imaging-based decisions, and Shapley Additive exPlanations were applied to quantitatively assess the importance of clinical features.
RESULTS: MEAF achieved robust performance in classifying cognitively normal, mild cognitive impairment, and AD, with an accuracy of 0.878 and an F1-score of 0.877. In the independent external validation cohort, MEAF maintained reasonable performance, with an accuracy of 0.817 and an F1-score of 0.803. Interpretability analyses provided complementary explanations for both the imaging and clinical models.
CONCLUSION: MEAF demonstrated favorable classification performance and interpretability in retrospective multicenter datasets, suggesting its potential as an auxiliary framework for multimodal assessment of AD-related cognitive staging.
METHODS: An interpretable Multimodal Ensemble Assessment Framework (MEAF) was developed to support clinical evaluation for AD across the cognitive spectrum. This framework employed a Swin Transformer and ResNet-50 for staged modeling of imaging features, applied machine learning techniques to extract clinical features, and used decision-level ensemble learning to integrate multimodal predictions. For interpretability analysis, Gradient-weighted Class Activation Maps were used to highlight key brain regions contributing to imaging-based decisions, and Shapley Additive exPlanations were applied to quantitatively assess the importance of clinical features.
RESULTS: MEAF achieved robust performance in classifying cognitively normal, mild cognitive impairment, and AD, with an accuracy of 0.878 and an F1-score of 0.877. In the independent external validation cohort, MEAF maintained reasonable performance, with an accuracy of 0.817 and an F1-score of 0.803. Interpretability analyses provided complementary explanations for both the imaging and clinical models.
CONCLUSION: MEAF demonstrated favorable classification performance and interpretability in retrospective multicenter datasets, suggesting its potential as an auxiliary framework for multimodal assessment of AD-related cognitive staging.